Criteria of good research problem pdf

In a service industry, customer satisfaction and the cost of providing service are fundamental conflicting criteria. On the other hand, when stakes are high, it is important to properly structure the problem and explicitly evaluate multiple criteria. In making the decision of whether to build a nuclear power plant or not, and where to build it, there are not only very criteria of good research problem pdf issues involving multiple criteria, but there are also multiple parties who are deeply affected by the consequences. Structuring complex problems well and considering multiple criteria explicitly leads to more informed and better decisions.

There have been important advances in this field since the start of the modern multiple-criteria decision-making discipline in the early 1960s. If not a Roman Numeral, then What? MCDM is concerned with structuring and solving decision and planning problems involving multiple criteria. The purpose is to support decision-makers facing such problems.

Solving” can be interpreted in different ways. Another interpretation of “solving” could be choosing a small set of good alternatives, or grouping alternatives into different preference sets. The difficulty of the problem originates from the presence of more than one criterion. There is no longer a unique optimal solution to an MCDM problem that can be obtained without incorporating preference information. The concept of an optimal solution is often replaced by the set of nondominated solutions. A nondominated solution has the property that it is not possible to move away from it to any other solution without sacrificing in at least one criterion.

Therefore, it makes sense for the decision-maker to choose a solution from the nondominated set. Generally, however, the set of nondominated solutions is too large to be presented to the decision-maker for the final choice. Normally one has to “tradeoff” certain criteria for others. MCDM has been an active area of research since the 1970s. There are different classifications of MCDM problems and methods. A major distinction between MCDM problems is based on whether the solutions are explicitly or implicitly defined.

These problems consist of a finite number of alternatives, explicitly known in the beginning of the solution process. Each alternative is represented by its performance in multiple criteria. One may also be interested in “sorting” or “classifying” alternatives. Some of the MCDM methods in this category have been studied in a comparative manner in the book by Triantaphyllou on this subject, 2000. In these problems, the alternatives are not explicitly known. Whether it is an evaluation problem or a design problem, preference information of DMs is required in order to differentiate between solutions. The solution methods for MCDM problems are commonly classified based on the timing of preference information obtained from the DM.

There are methods that require the DM’s preference information at the start of the process, transforming the problem into essentially a single criterion problem. These methods are said to operate by “prior articulation of preferences”. Methods based on estimating a value function or using the concept of “outranking relations”, analytical hierarchy process, and some decision rule-based methods try to solve multiple criteria evaluation problems utilizing prior articulation of preferences. Similarly, there are methods developed to solve multiple-criteria design problems using prior articulation of preferences by constructing a value function. Perhaps the most well-known of these methods is goal programming.

Once the value function is constructed, the resulting single objective mathematical program is solved to obtain a preferred solution. Some methods require preference information from the DM throughout the solution process. These are referred to as interactive methods or methods that require “progressive articulation of preferences”. Multiple-criteria design problems typically require the solution of a series of mathematical programming models in order to reveal implicitly defined solutions.

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